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2 2014 Vera Institute of Justice. All rights reserved. Additional copies can be obtained from the communications department of the Vera Institute of Justice, 233 Broadway, 12th floor, New York, NY 10279, (212) This report is available online at Please direct requests for additional information about this paper to Carl Matthies at

3 About This Paper This white paper describes and discusses the main methodological challenges to performing costbenefit analyses (CBAs) of justice-system investments. It is a product of the Vera Institute of Justice s national Cost-Benefit Knowledge Bank for Criminal Justice project. The paper was shaped with input from the Cost-Benefit Methods Working Group, which Vera convened in 2012 to help advance the use of rigorous CBAs to inform criminal-justice policy decisions. The goals of the working group were to: Foster the use of CBA in justice policy by providing information on cost-benefit methods to criminal justice researchers and practitioners; Provide guidance on some of the complicated areas of justice-related CBAs, such as measuring social benefits and addressing data limitations; Provide advice on increasing the policy relevance of CBA; and Investigate areas for further research. The working group consisted of researchers and policymakers with diverse backgrounds. Some members were drawn from the ranks of academia and possess a deep understanding of CBA methods and concepts, while others have prominent roles in justice planning and administration in state and local government, with firsthand knowledge of the questions policymakers need answered, the available data, and the political and practical dimensions of policy decisions. The working-group members were: Mike Clark, chief economist, Kentucky Legislative Research Commission Meredith Farrar-Owens, director, Virginia Criminal Sentencing Commission Lynn A. Karoly, senior economist, RAND Corporation Mike Lawlor, under secretary, Criminal Justice Policy and Planning Division, Office of Policy and Management, State of Connecticut Lee Ann Labecki, former director, Office of Research, Evaluation, and Strategic Policy Development, Pennsylvania Commission on Crime and Delinquency Kristin Misner, chief of staff, Office of the Deputy Mayor for Health and Human Services, City of New York John Roman, senior fellow, Urban Institute Diane E. Shoop, manager, Outreach and Policy Support, Pennsylvania Commission on Sentencing Ronald Villa, deputy chief operating officer, City of San Diego David L. Weimer, Edwin E. Witte Professor of Political Economy, University of Wisconsin Madison

5 Introduction Cost-benefit analysis is an economic assessment tool that compares the costs and benefits of policies and programs for the time they produce their impacts. The hallmark of CBA is that costs and benefits are both expressed in monetary terms so that they can be directly compared. CBA supplies policymakers with information to weigh the pros and cons of alternative investments and enables them to identify options that are cost-effective and will have the greatest net social benefit. Because benefits are always expressed in dollar terms, CBA also enables decision makers to compare policies and programs that have different purposes and outcomes. Although CBA is a well-established economic method, it has not been widely used in criminal justice. Promoting knowledge about CBA and addressing methodological issues specific to criminal justice can help increase the quantity and the quality of cost-benefit studies in the field. This report offers guidance on important conceptual and practical issues surrounding the use of CBA to inform justice policymaking. How This Paper Is Organized This white paper covers five topics: selecting perspectives to include in justice-related CBAs; predicting and measuring the impacts of justice initiatives; monetizing (that is, placing dollar values on) justice initiatives; dealing with uncertainty; and making cost-benefit studies clearer and more accessible. These are key elements of cost-benefit analysis and can be challenging in the criminal justice context. Who Should Read This Paper This paper is intended for anyone who conducts, plans to conduct, or wants to learn how to conduct a cost-benefit study of a justice-related policy or program. Researchers, evaluators, analysts (including legislative, policy, budget, and fiscal analysts), criminologists, and those in similar or related professions may find the paper useful. Readers who do not intend to get into the weeds of doing a CBA but are curious about its methods may find some passages rather technical. For a discussion focused more on the pragmatic aspects of applying CBA to policymaking, please see the 2014 companion paper, Using Cost-Benefit Analysis for Justice Policymaking. This white paper is not intended to be a comprehensive primer on cost-benefit analysis. Much more thorough treatments of the subject are available, covering a broader range of topics in greater depth and exploring the use of CBA in a wide variety of policy arenas. Readers are encouraged to avail themselves of these resources, some of which are listed in the References section. Readers will note that in some instances the paper gives advice or recommendations, while elsewhere it provides information readers may use to weigh decisions and communicate when explaining their work. This is primarily because, as stated earlier, not all issues pertaining to cost-benefit analysis in criminal justice have clear right or wrong answers. Vera Institute of Justice 1

6 Guiding Principles This paper is guided by six key principles adapted from the Society for Benefit Cost Analysis and endorsed by the Cost-Benefit Methods Working Group. 1 These principles are enumerated in Figure 1 and discussed below. Figure 1. Principles for cost-benefit analysis (CBA) and justice policymaking 1. CBA is a decision tool, not a decision rule. 2. Analysts should strive to quantify all impacts of a policy alternative relative to current policy, and to monetize costs and benefits for all members of society. 3. Transparency in a CBA enhances its value. 4. A CBA should disclose areas of uncertainty and clearly describe how uncertainty has been addressed. 5. The effort required for a CBA should not outweigh the expected value of the resulting information. 6. The pursuit of a perfect analysis should not prevent the completion of a useful one. 1. Cost-benefit analysis is a decision tool, not a decision rule. CBA can improve the decision-making process by providing a clear, systematic assessment of the decision s effects. CBA is not intended to replace the role of the decision maker or be used in lieu of existing decision-making processes. Rather, consider CBA a tool that adds information about the efficient use of resources to the other sources of information that decision makers rely on Analysts should strive to quantify all impacts of a policy alternative relative to current policy, and to monetize costs and benefits for all members of society. All policies generate costs and benefits. To accurately depict future conditions, cost-benefit analysts should attempt to quantify all impacts and monetize the costs and benefits of a proposed policy change relative to the status quo. But not all outcomes can be quantified or monetized. In such cases, analysts should document the qualitative outcomes along with the quantitative results. The analysis should also discuss who is affected by the current policy and who would be affected by the policy alternative. 3. Transparency in a CBA enhances its value. 1 Richard O. Zerbe, Tyler Blake Davis, Nancy Garland, and Tyler Scott, Toward Principles and Standards in the Use of 2 Matthew J. Kotchen, Cost-benefit analysis, in Encyclopedia of Climate and Weather, second edition, edited by Stephen H. Schneider, Michael Mastrandrea, and Terry L. Root (Oxford, UK: Oxford University Press, 2011). Vera Institute of Justice 2

7 Transparency makes a CBA replicable and helps foster the reader s trust. Assumptions and calculations should be sufficiently clear and detailed in the analysis to allow technical audiences to replicate it or, at a minimum, understand the steps taken in the analysis. When readers trust a CBA, their focus can shift from the technical details to the decision-making process. 3 Transparency also relates to clarity, that is, presenting the analysis in a way that is understandable and meaningful to policy audiences. 4. A CBA should disclose areas of uncertainty and clearly describe how uncertainty has been addressed. A cost-benefit study is stronger when its authors address uncertainty. Some uncertainty is inevitable when forecasting policy effects and monetizing costs and benefits. Uncertainty should not paralyze the analysis; rather, analysts should explicitly acknowledge and address it by exploring the sensitivity of net benefits to the assumptions and estimates that they employed. They should document any uncertainty about predictions and valuations, as well as other assumptions that might affect the policy recommendation. They should also discuss the sensitivity techniques they used to address uncertainty. 5. The effort required for a CBA should not outweigh the expected value of the resulting information. Some key issues can be addressed by using another less time-consuming form of economic analysis, such as cost-effectiveness analysis or fiscal-impact analysis. Before conducting a CBA, decision makers and analysts should consider whether the answer to the question being asked is worth the effort that will be expended to conduct the analysis. Moreover, when conducting a CBA, the analytic effort should focus on the factors most likely to affect the results. 6. The pursuit of a perfect analysis should not prevent the completion of a useful one. Perfection is an unattainable and therefore unreasonable standard in economic analysis. A CBA that meets basic requirements regarding objectivity and methodology can provide valuable information and contribute to the decision-making process, so long as the study s limitations are explicit. 4 3 Richard O. Zerbe et al., 2010, p Ibid., p. 36. Vera Institute of Justice 3

8 A few notes about terminology Throughout this paper, important terms are italicized in the text and defined in the glossary. The authors assumed a basic understanding of budgeting vocabulary and its usage among readers. (For example, this paper doesn t spell out what it means to use cost as a verb, but does explain micro-costing.) A note about the word program and similar terms: Although program, policy, project, intervention, and initiative aren t by definition interchangeable, this paper often uses one of those words when another could apply. Justice-related programs, policies, projects, interventions, and initiatives can all be subject to CBA, and all five terms are used in this paper. Section I: Perspectives A distinguishing feature of cost-benefit analysis is its comprehensiveness. Whereas other forms of economic analysis, such as fiscal-impact studies, typically adopt the narrow perspective of a single government agency, CBA aims to capture the costs and benefits to all parties affected by the policy under examination. This means including not only the perspectives of organized stakeholders, but of all relevant members of society, to provide a complete view of the policy s impact. Parties whose perspectives are included in a CBA are said to have standing in the analysis. In practice, studies may not account for all perspectives, given the many challenges of conducting rigorous CBAs, as well as diverse opinions about which perspectives matter in an analysis. We recommend that: Justice CBAs should include at least the taxpayer and crime-victim perspectives. CBAs should be explicit about any relevant perspectives that are excluded and the reasons for the exclusion. When reporting the results of a CBA, analysts should disaggregate and display the costs, benefits, and net benefits (or other metrics) for each perspective included in the study. This section of the white paper provides some background on perspectives including the perspectives typically included in justice-related CBAs and explains how to select the perspectives to include in a study. Taxpayers Whether they introduce new programs, change sentencing laws, or attempt to make the justice system more efficient, justice-related policies affect taxpayers, who fund government operations. Cost-benefit studies therefore routinely include the taxpayer perspective in their calculations. The taxpayer perspective is synonymous with the government perspective, although these parties don t always agree about how public funds should be spent. Still, it is appropriate to treat the government and taxpayer perspectives as equivalent in CBA. Bear in mind that the taxpayer perspective includes all agencies affected by a program or policy, not just those that directly involve Vera Institute of Justice 4

9 criminal justice. (See Taxpayer Costs on page 19 for more information on how to estimate costs and benefits for this perspective.) Crime Victims Because public safety is often the central goal of justice policies, the perspective of crime victims is a critical part of justice-related CBAs. Crime victims may experience financial, psychological, and physical harms. These harms can be expressed in dollars as tangible costs, such as medical expenses and lost property, and as intangible costs, such as fear of crime or pain and suffering. When victimization is prevented or reduced, the avoided harms are counted as benefits. (See Victim Costs on page 21 for information about how to estimate the values of tangible and intangible costs and benefits to victims.) Offenders An ongoing debate among analysts is about when to include the perspective of offenders in justicerelated CBAs. Although there is no hard-and-fast rule, most in the field agree that offenders should not have standing with respect to their illegal activities, precisely because the activities are prohibited. Otherwise, the CBA would be at odds with the rule of law, a scenario that could lead to the absurd interpretation of stolen property as merely a transfer of goods from the victim to the offender, with no net change in social welfare. 5 Thus, the benefits criminals derive from engaging in illegal activity should not typically be deducted from the total benefits of a crime-reduction program. Studies that analyze the decriminalization of victimless offenses are a different story: when evaluating proposals to legalize, tax, and regulate prostitution or marijuana, for example, the anticipated benefits to producers and consumers in these black markets should be taken into account, because under the new law these parties would no longer be engaging in illegitimate activities. The offender perspective is multifaceted, and just as most economists argue that offenders lack standing with respect to criminal activities and costs incurred from legitimate punishment for those crimes, there is widespread agreement that offenders do have standing as participants in the criminal justice system. The rationale for including the offender perspective is that if CBA is used to decide between two programs with the same impact and benefits to the rest of society, the program that leads to better outcomes for offenders is preferable. Thus, CBAs of justice-related programs such as reentry services for formerly incarcerated persons or a prison education program should capture the costs and benefits to program participants. Consider a program that teaches job skills to people in the last months of their prison sentence. The program may confer future benefits to participants in the form of better job prospects and higher earnings. But the program may impose costs such as fees, transportation expenses, or even lost wages, if participants could have been earning income instead. 5 Andrew S. Rajkumar and Michael T. French, Drug Use, Crime Costs, and the Economic Benefits of Treatment. Journal of Quantitative Criminology 13, no. 3 (1997), Vera Institute of Justice 5

10 The Rest of Society Taxpayers, crime victims, and offenders are not the only groups affected by criminal justice policies. For instance, reduced crime might benefit communities and businesses through increased property values and commercial activity. Defendants released to pretrial supervision might be able to keep their jobs. Children whose parents complete a drug treatment program might perform better in school. CBAs sometimes refer to parties affected indirectly as society or the societal perspective, even though society includes everyone. In some cases, societal costs and benefits may be hard to quantify because of insufficient information to isolate the impacts of a policy on all members of society. At other times the impacts may be difficult to convert into dollar values. (See Section III on page 18 for a description of methods used to measure the value of an outcome or impact in dollars.) Figure 2 illustrates the trade-off between comprehensiveness and analytical effort. Figure 2. The trade-off between comprehensiveness and analytical effort in cost-benefit analysis Source: Adapted from Alex Cowell, Approach for Conducting Cost, Cost-Effectiveness, and Benefit-Cost Analyses of SVORI programs. Research Triangle Park, NC: RTI International, (accessed January 16, 2014). The societal perspective also comes into play when justice policies or programs have broad economic repercussions, known as general equilibrium effects. For instance, draconian drug laws could spur enough growth in incarceration to trigger new prison construction and affect the price of concrete and other building materials. Another example would be a highly successful large-scale reentry program Vera Institute of Justice 6

11 that provides vocational training. The program s graduates could displace members of the existing workforce; with fewer graduates committing new crimes, declining inmate populations could result in prison closures and job loss in the corrections sector. It is uncommon, however, for justice policies and programs to have an impact large enough to cause general equilibrium effects. Naturally, there is overlap among people s roles and their perspectives. For example, a person who commits an offense may be a taxpayer as well as a victim of crime. In reality these roles can and do coexist, however, those nuances aren t typically captured in the context of CBA. Selecting Perspectives to Include in a CBA The perspectives that analysts include or exclude in a study can affect its results such as whether the CBA has a net benefit and if so, the size of the net benefit. That is why analyses should be as comprehensive as possible and include all relevant perspectives. Figure 3 illustrates how net benefits can change depending on the perspectives included. For example, the net benefit of adult drug courts per participant in the state of Washington is $4,767 if taxpayer and victim perspectives are included, but drops dramatically to $372 when only the taxpayer perspective is included. Figure 3. Options to reduce crime in Washington State: Benefits and costs by perspective, per participant (2006 dollars) TAXPAYER COSTS BENEFITS NET BENEFITS (BENEFITS MINUS COSTS) For taxpayers For victims For taxpayers Total Adult drug courts $4,333 $4,705 $4,395 $372 $4,767 Intensive supervision: treatment-oriented programs $7,124 $9,369 $9,318 $2,245 $11,563 Vocational education in prison $1,182 $6,806 $8,114 $5,624 $13,738 Source: Steve Aos, Marna Miller, and Elizabeth Drake, Evidence-Based Public Policy Options to Reduce Future Prison Construction, Criminal Justice Costs, and Crime Rate (Olympia: Washington State Institute for Public Policy, 2006), No , (accessed February 3, 2014). But practical or methodological limitations may make a comprehensive approach impossible. When the benefits and costs associated with a perspective can be credibly measured, the perspective should be included in the CBA s calculations. When there isn t a reliable way to estimate quantitatively how a perspective is affected by a policy or to place a monetary value on that effect the study should describe the costs and benefits from that perspective qualitatively. Most justice-related CBAs report results from the perspectives of taxpayers, victims, and, when applicable, offenders (for example, when they are program participants), while excluding or simply alluding to other costs and benefits. Vera Institute of Justice 7

12 Apart from the practical challenges of conducting a thorough study, the question of who has standing in a CBA is potentially contentious. Some policymakers may be interested in only the taxpayer perspective, viewing these costs and benefits as the most salient because they have the most clear-cut fiscal or budgetary impacts. But analysts should strive to present all relevant perspectives and disaggregate costs and benefits by perspective to give decision makers the ability to weigh them as they see fit. At a minimum, analysts should document the perspectives they included in a study and explain which ones they excluded or discussed only qualitatively, as well as how they made those choices. Keep in Mind When reporting the results of a cost-benefit analysis, consider displaying the costs, benefits, and net benefits (or other metrics) for each perspective, as in Figure 3 above. Disaggregating the information this way improves transparency by allowing readers to see each party s costs and benefits, and the contribution of each perspective to the CBA s bottom line. Section II: The Evaluation Component of CBA: Predicting or Measuring Program Impacts Cost-benefit analysis requires predicting or measuring the effects of policy alternatives. Many sources of information can help to make these predictions or measurements, including evaluations of similar policies in the decision maker s jurisdiction or elsewhere. 6 CBA uses evaluation as an empirical basis for assessing the impacts, both beneficial and detrimental, of a program. These effects are then translated into dollar amounts to determine the program s net present value. CBA has thus been characterized as the merger of evaluation and valuation and also described, in the crime-prevention context, as a second-generation evaluation tool, with a prerequisite of rigorous evaluation to determine whether programs had any detectable impact on crime. 7, 8 Not every person conducting a cost-benefit study of a justice program will be in the position to evaluate the program. But any person conducting or planning a CBA will need to understand some basics about evaluation to perform a high-quality economic study. A quality CBA based on poorly done evaluations could yield spurious results and undermine the study s credibility. This section describes barriers to evaluation, three basic ways to estimate program impacts, the fundamental elements of a good evaluation design, meta-analysis, and some of the common pitfalls in evaluating justice programs. 6 Anthony E. Boardman, David H. Greenberg, Aidan R. Vining, and David L. Weimer, Cost-Benefit Analysis: Concepts and Practice, third edition. (Upper Saddle River, NJ: Prentice Hall, 2011), Edwin W. Zedlewski, Conducting Cost Benefit Analyses in Criminal Justice Evaluations: Do We Dare? European Journal on Criminal Policy and Research, Special Issue: The Costs of Crime 15, no. 4 (2009), John Roman and Graham Farrell, Cost-Benefit Analysis for Crime Prevention: Opportunity Costs, Routine Savings and Crime Externalities, Crime Prevention Studies 14 (2002), Vera Institute of Justice 8

13 Barriers to Evaluation Not every program is amenable to evaluation. For example, some justice-involved populations are exposed to a suite of programs. With an eye on the budget, policymakers may want to know the effect of each program. Yet in such cases the effects of individual programs cannot be disentangled. This is what is known as an identification problem. It is frustrating for researchers and practitioners to accept that a program or policy s effects are in a sense unknowable. Thus it is critical to guard against the temptation to amass more yet uninformative data or to make unfounded assumptions or leaps of logic in a vain attempt to overcome an identification problem. The existence of an entire sub-discipline of program evaluability assessments testifies to the importance of recognizing this type of barrier. 9 For programs that do not have an inherent identification problem, a second challenge remains: access to data of adequate quantity and quality. Sometimes this is merely a matter of patience. It would be inappropriate to evaluate a probation program aimed at reducing recidivism two weeks after it commences, unless by then it is already an obvious failure; more time is needed for results to be meaningful. At other times, if not enough people are exposed to a program to make valid inferences about its impacts, as with highly specialized programs, the problem is referred to as insufficient statistical power. 10 Getting the right data can also present a barrier to program evaluation. Important outcomes are often difficult to measure reliably and may not be captured in existing data. How much crime is prevented by notification systems that alert victims to the imminent release of the offenders who victimized them? This is not an easy outcome to measure. It can therefore be tempting to build an evaluation using data that can be readily captured, even though that data is an ambiguous measure of effectiveness. For example, changes in the number of calls for service and the number of arrests are not per se evidence that a crime-control program is or is not working. Similarly, conviction rate is not an especially useful outcome for gauging a jurisdiction s adherence to due process. How Impacts Are Estimated Another major area of confusion over program evaluation is about which methods produce reliable estimates of effect size. Program evaluations can have a non-experimental (that is, observational), experimental, or quasi-experimental design. Each design has advantages and disadvantages. Figure 4 summarizes the three evaluation methods and provides a justice-related example from the research literature for each method. Non-Experimental Design Justice programs are often evaluated by comparing specific outcomes before and after implementation. This approach is relatively easy and intuitively appealing. For example, a police department might measure the impact of a gang task force by comparing the number of gang-related crimes in its 9 For more information on identification problems in social science, see Charles Manski s books Identification Problems in the Social Sciences (Cambridge, MA: Harvard University Press, 1995) and Identification for Prediction and Decision (Cambridge, MA: Harvard University Press, 2007). 10 For more information on statistical power, see Paul Ellis s book The Essential Guide to Effect Sizes: Statistical Power, Meta-Analysis, and the Interpretation of Research Results (Cambridge, UK: Cambridge University Press, 2010), Vera Institute of Justice 9

14 jurisdiction before the task force was created to the number of gang-related crimes committed after its formation. The program s effectiveness would be equated with the percentage change in the number or rate of gang-related crimes. Although it is reasonable to suppose that the task force would have a significant impact on gang crime, there is nothing in the pre-test and post-test comparisons that eliminates or reduces the possibility of alternative explanations. The decline in gang crime could be the result of collective efficacy in gang-afflicted communities, a voluntary cease-fire negotiated by warring gangs, a delayed impact of routine policing, or some other coincident factor. Attributing the entire impact to the task force would require a strong assumption that no other factor played a role in the observed change. Because these confounding factors cannot be ruled out, the basic pre-test and posttest comparison is considered a non-experimental method. As a second example of a non-experimental evaluation design, consider a scenario in which agency leaders want to know how a community policing program has shaped constituents views about law enforcement. This is a hard construct to quantify. Suppose the department mailed a survey on attitudes about local police to residents who had called them for assistance. The results of such a survey should not be interpreted as a reliable measure of constituent attitudes overall, because people who call the police for assistance may be predisposed to hold a favorable view of law enforcement. By limiting the evaluation to this population, the agency would have introduced a selection bias. This bias would not necessarily be eliminated by mailing the survey to everyone, as attitudes toward police may differ between those who submit responses and those who do not. To meet the scientific standard for demonstrating causality, program evaluations must provide a credible counterfactual, that is, a sense of what would have occurred without the program. In the first example, the counterfactual is, What would have happened to gang crime numbers in the absence of the task force? a different question from What were gang crime numbers before the task force? The counterfactual in an impact evaluation is represented by a control group or comparison group, which should resemble the individuals, neighborhood, or jurisdiction that is exposed to the program as closely as possible. Experimental Design The experimental design method for identifying program impacts is to conduct a randomized controlled trial (RCT). RCTs are the gold-standard method for estimating program effect sizes because they approximate laboratory conditions. The act of randomizing program exposure creates a nearperfect counterfactual or control group. If participant and non-participant individuals, neighborhoods, or jurisdictions (as the case may be) are selected at random, in a large enough sample, the two groups should be essentially equivalent. As a result, any difference in outcome between the groups is for the most part attributable to the program s effects. However, attrition from one or both groups may not be random, in which case the groups are no longer equivalent. For instance, if the program is demanding, some participants may drop out before reaping all of its ostensible benefits. Under such circumstances, the outcomes for those who fail to complete the program should still be tracked; this estimated impact becomes the intent-to-treat effect. Also bear in mind that although RCT is the most rigorous standard for estimating an effect size within a sample population, this does not necessarily mean that the result can be generalized to other populations or settings. For example, the estimated impact of a program Vera Institute of Justice 10

15 Figure 4. Overview of evaluation methods Description Advantages Disadvantages Example Experimental Design Randomized Controlled Highest internal validity because it May be impractical or infeasible. May In Philadelphia, legal representation for indigent Trial guards against systematic differences raise ethical concerns of denying defendants accused of murder is essentially Individuals are randomly between groups. With sufficient services to control group. assigned at random: one in every five is assigned to a treatment group sample size, true random assignment represented by a public defender, while the and a control group. negates confounding factors, observed remainder have court-appointed private attorneys. or unobserved. Anderson and Heaton (2012) were able to exploit this system to compare the efficacy of two sources of criminal-defense counsel in terms of verdicts and sentencing. 11 Quasi-Experimental Design Instrumental Variables Enables causal inference when a In practice it can be difficult to find Cook and Ludwig (2004) estimated the social This method is usually a two- RCT is infeasible. Can correct issues valid instrumental variables and to cost of gun ownership by examining the stage regression in which the with omitted variables, selection bias, prove their validity. relationship between household gun-ownership first regression involves using and reverse causality to give an prevalence and homicide rates for the 200 largest one or more variables to unbiased, consistent effect-size U.S. counties. 12 To adjust for the reverse predict a treatment variable estimate by removing the variation in causation of crime and gun prevalence (i.e., and the second regression the treatment variable that is crime can affect gun prevalence and vice versa), uses first-stage predicted correlated with the regression error the authors used the counties rural population in values of the treatment to term. An instrumental variable 1950, a number correlated with current homicide predict the outcome of should affect only the outcome of rates solely through its correlation with current interest. interest through its correlation with household gun prevalence. the treatment. 11 James A. Anderson and Paul Heaton, How Much Difference Does the Lawyer Make? The Effect of Defense Counsel on Murder Case Outcomes, RAND Working Paper (Santa Monica, CA: RAND Corporation, 2012), WR Philip J. Cook and Jens Ludwig, The Social Costs of Gun Ownership, The Sanford School Working Paper Series SAN04-07; National Bureau of Economic Research Working Paper No (Durham, NC: Duke University, 2004), (accessed January 16, 2014). Vera Institute of Justice 11

16 Description Advantages Disadvantages Example Quasi-Experimental Design (continued) Propensity Score Matching Reduces bias arising from differences This method creates a of observable characteristics between comparison group based on the program and comparison groups. observable characteristics that Enables creation of a comparison are predictive of assignment group even without complete overlap to the treatment group. in the observed traits of treated and Program effects are estimated untreated groups. Often the most by comparing outcomes for feasible method for programs that treatment-group members were not implemented with relative to comparison-group evaluation in mind. members with the same or nearly the same score. Difficult trade-off between matching on enough variables and what s known as the curse of dimensionality (too many traits to find good matches for all treated individuals). By definition the method controls only for observed factors that may affect assignment to treatment and outcome. Any unobserved factors that affect assignment to treatment and possibly outcome cannot be accounted for in the matching procedure. Sampson et al. (2006) attempted to disentangle the effect of marriage on criminal activity by matching individuals within a cohort of males born in Boston between 1924 and 1932 who had been adjudicated as juvenile delinquents. 13 Propensity scores were based on 20 traits, including IQ score, extroversion, age at first arrest, and parents criminality. The authors found that married men were, on average, 35 percent less likely to engage in criminal activity than their matched unmarried counterparts. Regression Discontinuity Can yield an unbiased estimate of the Can be difficult to attain adequate Berk and Rauma (1983) studied the impact of This method compares subjects treatment effect without statistical power; assumes a linear and transitional aid on parolee outcomes. Parolees who fall immediately above and below some threshold randomization. Well suited to realities of program implementation, in which parallel relationship between assignment variable (e.g., test score) must have worked 652 hours over a 12-month period of incarceration to be eligible for aid. 14 such as age, test score, or treatment assignment is seldom and the outcome variable for treated and The recidivism rate was approximately 13 income used as the sole random and is often based on a untreated groups. percent lower among people who received criterion for assignment into threshold value. transitional aid. treatment and control groups. 13 Robert J. Sampson, John H. Laub, and Christopher Wimer, Does Marriage Reduce Crime? A Counterfactual Approach to Within-Individual Causal Effects, Criminology 44, no. 3 (2006), Richard A. Berk and David Rauma, Capitalizing on Nonrandom Assignment to Treatments: A Regression-Discontinuity Evaluation of a Crime-Control Program, Journal of the American Statistical Association 78, no. 381 (1983), Vera Institute of Justice 12

17 Description Advantages Disadvantages Example Quasi-Experimental Design (continued) Difference-in-Differences Provides a more rigorous test of This method entails a pre-test treatment effect than simple beforeand-after comparisons; can be and post-test comparison of matched treatment and control designed to account for confounding groups to account for other factors that might explain the factors that affect both groups differences in outcomes between and could influence outcomes. groups. Assumes that the treatment and control groups would have followed the same trajectory but for the treatment. In other words, this method usually does not control for unobserved time-variant differences in the groups, a factor that also may have influenced treatment decision. La Vigne et al. (2011) measured the impact of public surveillance cameras in Chicago and Washington, DC, by comparing the net change in crime for areas with cameras after accounting for, or differencing out changes in comparable control areas. 15 The researchers also measured crime changes in areas adjacent to the cameras, to test for displacement of crime or diffusion of benefits. Pre-Test and Post-Test Straightforward and relatively Does not control for unobserved factors Cochran et al. (1994) compared homicide counts Comparison inexpensive. Reasonable when other that may affect both the receipt of in Oklahoma before and after the death penalty This is a comparison of the factors are unlikely to affect the treatment and the outcome(s). Some pre- was reinstituted and a high-profile execution was same group or unit of analysis outcome, when the same trend in test and post-test designs may carried out. 16 Stolzenberg and D Alesso (1994) before and after a given outcome is seen repeatedly, and when approximate quasi-experiments by examined the long-term effect of Minnesota s treatment or intervention. experimental or other quasi- taking into account seasonal and other sentencing reform on reducing unwarranted experimental designs are infeasible. time-related trends. (i.e., not legally mandated) sentencing disparities using an interrupted time series Nancy La Vigne et al., 2011, ix-xii. 16 John K. Cochran, Mitchell B. Chamlin, and Mark Seth, Deterrence or Brutalization? An Impact Assessment of Oklahoma s Return to Capital Punishment, Criminology 32, no. 1 (1994), Lisa Stolzenberg and Stewart J. D Alessio, Sentencing and Unwarranted Disparity: An Empirical Assessment of the Long-Term Impact of Sentencing Guidelines in Minnesota, Criminology 32, no. 2 (1994), Vera Institute of Justice 13

18 designed to teach business skills to a random sample of inmates with demonstrated intellectual ability and pro-social behavior should not be generalized to the entire inmate population, which may not share those traits. Setting up an RCT for program evaluation purposes can be costly and time-consuming, although opportunities for low-cost experiments occasionally arise. Programmatic treatments are randomly assigned for reasons other than an evaluation, such as when capacity is limited. Researchers can take advantage of the randomization and make use of routinely collected administrative data to conduct a low-cost rigorous evaluation. 18 Nevertheless, ethical issues and operational realities often make an RCT impossible or highly impractical. Fortunately, there are quasi-experimental methods that can provide credible estimates of program effects. These research designs rely on the existence of a credible counterfactual created by researchers or by chance that mimics experimental conditions. Quasi-Experimental Design Quasi-experimental evaluations frequently rely on some form of regression analysis to estimate effect sizes. Regression is a statistical modeling technique used to calculate how changes in one or more factors, called independent variables, correlate with changes in an outcome of interest, called the dependent variable. In the context of program evaluation, the independent variable of greatest interest is a treatment variable indicating whether the unit of analysis was part of the treatment group or part of the control group. The estimated amount by which the treatment variable changes the outcome is the effect-size estimate. Regression analysis is a highly versatile method. The outcome modeled can be discrete counts (e.g., monthly burglary reports for a law enforcement agency), continuous (e.g., court processing time from arraignment to sentencing), ordinal (e.g., a fear of crime scale ranging from 1 to 5), or probabilistic (e.g., likelihood of recidivism within three years post-release). Similarly, depending on the circumstances, the treatment can be a yes-or-no (binary) variable, or a categorical or continuous variable that reflects the treatment dosage. An all too common bias called simultaneity arises when an explanatory variable is determined jointly with the dependent variable. As an illustration, Figure 5 is a scatter plot of index crime rates on police officers per capita for large U.S. cities. Notice that officers per capita appears to be positively correlated with the index crime rate (i.e., the regression line through the data slopes upward), contrary to expectations if one believes that having more officers helps to control crime. The scatter plot illustrates how a simple regression analysis can be misleading. The number of police and the index crime rate affect each other. The number of police per capita may reduce crime, but increases in crime may cause policymakers to increase the size of the police force. 18 Coalition for Evidence-Based Policy, Rigorous Program Evaluation on a Budget: How Low-Cost Randomized Controlled Trials Are Possible in Many Areas of Social Policy (Washington, DC: CEBP, 2012), (accessed February 3, 2014). Vera Institute of Justice 14

19 Figure 5. Relationship between police-force size and crime in large U.S. cities, 2007 Source: Paul Heaton, Hidden in Plain Sight: What Cost-of-Crime Research Can Tell Us About Investing in Police (Santa Monica, CA: Rand Corporation, 2009), (accessed February 3, 2014). Instrumental variables. Analysts can overcome the simultaneity problem by introducing instrumental variables, a method that usually involves a two-stage regression. The first regression uses one or more variables to predict a treatment variable. The second regression uses the predicted values of the treatment variable from the first stage to predict the outcome of interest. An instrumental variable is a variable whose only effect on the outcome is through its effect on the treatment variable. In the example of the relationship between crime and police-force size, terrorist events and election years can be used as instrumental variables; they present situations in which the size of a police force is at least temporarily increased without regard to the crime rate. By first regressing police-force size on the instruments, one obtains predicted values for police per capita that are uncorrelated with the index crime rate. The outcome can then be regressed on the predicted values to obtain unbiased estimates of the impact of police on crime. Finding valid instruments can be challenging. The instrument must strongly predict the explanatory variable that is jointly determined with the outcome, while having no discernible relationship to other explanatory variables in the regression model, thus demonstrating quasi-random conditions. Propensity score matching. For programs that target individuals, such as electronic monitoring or pretrial diversion, a technique called propensity score matching is sometimes used to ensure that program participants are compared to the subset of non-participants who are most similar to them, thus reducing bias. The propensity score reflects the degree of similarity (on observed characteristics) between any program participants and non-participants, and serves as the basis for matching. The score Vera Institute of Justice 15

20 is derived by estimating the impact of these observed characteristics on the probability of receiving the treatment. A glaring potential problem with the propensity score method is that treatment and control individuals may differ markedly in ways that are not observed. Regression discontinuity. In some instances, program assignment is determined by a threshold score on a risk- or needs-assessment test. If the cutoff score is somewhat arbitrary and a sufficient number of people score just above or below the cutoff, the effect of the program can be estimated from the difference in outcomes for these barely eligible and ineligible groups. This method is known as regression discontinuity. Difference-in-differences (DiD). This approach compares net changes between treatment and control groups, before the treatment or intervention versus afterward. Static (time-invariant) differences between the groups are factored out of the equation, while observable time-variant differences can be included in the regression model. Unobserved differences that change over time can be controlled for through inclusion of a second control group to generate a triple-differences estimate. For example, Heaton estimated the public-safety impact of repealing Sunday liquor laws in parts of Virginia using the difference in crime rates before versus after repeals took effect, on Sundays versus other days of the week, and in affected versus unaffected jurisdictions. 19 Looking at other days of the week unaffected by the repeal controlled for unobserved changes within repeal jurisdictions over time, such as a local increase in police personnel. Looking at jurisdictions that did not repeal the law controlled for unobserved changes that might affect crime on Sundays throughout Virginia, such as the release of large numbers of inmates to alleviate state prison overcrowding. DiD is often applied to place-based interventions, thus such an evaluation method might be appropriate for the aforementioned gang taskforce study (see Non-Experimental Design, page 9), as long as one or more neighborhoods with a 20, 21 similar gang problem unaddressed by the task force exist as a comparison group. Pre-test and post-test comparison. This approach can be considered quasi-experimental under some circumstances. An intervention likely to have an immediate observable impact can be analyzed in a manner similar to regression discontinuity, by comparing brief periods immediately before and after its implementation to mitigate the confounding effect of time Meta-Analysis When an agency or jurisdiction considers implementing a program that has been used and evaluated elsewhere, meta-analysis can indicate the expected effect size. Meta-analysis uses the results of prior studies to estimate a program s average impact, which can then be used in a CBA. Although it is less labor-intensive than carrying out an evaluation, meta-analysis still requires 19 Paul Heaton, Sunday Liquor Laws and Crime, Journal of Public Economics 96, nos. 1-2 (2012), Jeffrey Grogger, The Effects of Civil Gang Injunctions on Reported Violent Crime: Evidence from Los Angeles County, The Journal of Law and Economics 45, no. 1 (2002), Nancy G. La Vigne, Samantha S. Lowry, Joshua Markman, and Allison Dwyer, Evaluating the Use of Public Surveillance Cameras for Crime Control and Prevention (Washington, DC: Urban Institute, 2011), 17. Vera Institute of Justice 16

21 calculations and scrutiny of the literature, weighting the results according to the strength of the evaluation. If an agency is considering a program that has been meta-analyzed, it is still advisable to verify that the meta-analysis was done well. A meta-analytic review must include the following: Clear, logical criteria for inclusion and exclusion of studies; An explicit and sufficiently thorough search strategy; and Systematic coding and analysis of the studies included. Meta-analysis has been applied to a number of justice investments, such as corrections-based education, vocation, and work programs for adult offenders. 22 They searched eight literature databases and contacted fellow researchers to find relevant published and unpublished studies. Studies were excluded unless they 1) evaluated an educational, vocational, or work program for convicted offenders; 2) estimated the program s impact on some measure of recidivism; 3) included a control or comparison group, and 4) were published in English after Studies were weighted according to the estimate precision, follow-up period, and type of recidivism outcome. (For example, reconviction was weighted more heavily than rearrest.) Effect sizes were standardized as odds ratios the proportion of program and comparison participants who recidivated. Although the effect size was statistically significant, the authors declared the results inconclusive, arguing that selection bias in the program groups was potentially strong enough to account for the difference in outcomes. Meta-analysis has the appeal of deriving an estimate from a number of studies. It might then bolster the decision makers confidence in a program or policy s effectiveness, much like consulting several independent ratings reports before buying an appliance reassures consumers of its quality. But meta-analysis results may over-summarize and should be interpreted cautiously. The impact of a small pilot program evaluated several years ago might not correspond to the impact of the more extensive version planned for another jurisdiction, even if the programs otherwise seem very similar. Effects of time, place, and scale, as well as subtle differences in how the programs are implemented, can lead to disparities between an estimated and an actual impact. Keep in Mind Program evaluations pose numerous challenges, the first of which is that an evaluation may not even be feasible. For instance, law enforcement interventions, such as intelligence-led policing, can be difficult to study. These programs may represent a series of incremental policy changes that become a different way of doing things over time. CBA s goal of comprehensiveness makes program evaluation even more demanding. Programs often have ramifications upstream and downstream in the justice system, and measuring these impacts can require linking data from disparate sources. Important long-term benefits of justice programs, like educational attainment and employment, may not be captured in a traditional evaluation due to time and budget constraints or because the information is gathered by a government agency not involved in 22 David B. Wilson, Catherine A. Gallagher, and Doris L. MacKenzie, A Meta-Analysis of Corrections-Based Education, Vocation, and Work Programs for Adult Offenders, Journal of Research in Crime and Delinquency 37, no. 4 (2000), Vera Institute of Justice 17

22 the evaluation. Nonetheless, measuring such impacts would be vital for predicting benefits and, therefore, for determining the program s net present value. The reliance on recidivism as an outcome also presents a challenge. Although recidivism is the best index available for measuring the impact of programs on people s propensity to reoffend, it is an imperfect measure of criminal activity. There is no consistent definition of recidivism. Law enforcement agencies may count rearrest as recidivism, courts may use reconviction as the definition, correctional agencies may use recommitment, and probation departments may include technical violations in their definition. For a court system that is heavily backlogged, it will take longer for reconviction and recommitment data to accumulate, but using rearrest will overstate the burden of recidivism on courts and corrections. As with many other issues that arise in conducting evaluations for CBAs, there is no clear right or wrong answer, but there is an obligation to be clear about how terms are defined and how those definitions can affect the results. Section III: Valuing the Costs and Impacts of Justice Policies and Programs Quantifying program impacts is the end product for many social science studies. But with CBA, both the impacts and the resources expended to achieve them must be monetized to the extent possible. As discussed in Section I, programs can affect government spending, crime victimization, participants, and overall social welfare. Monetizing the positive and negative impacts to all relevant perspectives is the crux of CBA s contribution to economic analysis. But deriving credible dollar-value estimates of some harms and benefits is also one of the biggest challenges in justice-related CBAs. This section includes a discussion about estimating the costs of implementing a justice policy or program and then focuses on valuing the impacts on taxpayers, victims, offenders, and the rest of society. Our recommendations, many of which follow from those laid out in Section I, are that analysts should do the following: Use appropriate cost figures, being careful in particular to use marginal costs rather than average costs for initiatives that will affect government spending and therefore taxpayers on the margin. Examine all the resources used to implement a policy or program, including the time costs of employees and other stakeholders, when estimating the cost of a justice initiative. Assess impacts beyond those to the criminal justice system, when applicable. Disaggregate taxpayer costs by unit or level of government, when appropriate. Explain the methods and sources used for all calculations. Provide qualitative information on impacts and the monetary values of impacts when quantitative information is unavailable or unreliable. Program Costs Justice system programs and policies involve allocating resources that would otherwise be put to some alternative use. In the same way that defining the treatment and counterfactual are prerequisites for Vera Institute of Justice 18

23 program evaluation, distinguishing the cost of an initiative or intervention from business as usual operations is essential when estimating program costs. (See Non-Experimental Design, page 9.) Intrepid accounting is necessary to estimate accurately the cost of a program or policy relative to the status quo or other options, as policies and programs may receive public and private funding and can involve collaboration among multiple agencies and nongovernmental organizations. A wide range of resources may be used and costs incurred in the implementation of justice policies and programs. Direct costs such as staff salary, benefits, office supplies, and other equipment apply to virtually every program. Similarly, when a new facility is built specifically for a program, capital expenses like project planning, real estate, and construction should figure in the program cost estimate, along with indirect costs and start-up costs for furniture and equipment. Determining the cost of a criminal justice initiative can be a straightforward process. Some programs have dedicated resources, such as equipment and a certain number of full-time employees of known pay grade. Perhaps the program is neatly compartmentalized as one or more line items in a budget or is funded entirely through a grant. But even in these instances, analysts should know that what is appropriated is not always the same as what is spent. Additionally, the cost to taxpayers may not represent the full program cost if the program makes use of donated goods and services. For instance, the full cost of a job-training program for inmates that uses government employees as trainers, volunteers as mentors, and donated supplies should include taxpayer costs as well as the economic value of donated time and supplies. In other instances, estimating the costs of a criminal justice policy or program is more complex, requiring a careful assessment of the personnel and agencies involved. One approach to estimating employee time and other variable program costs is transactional and institutional cost analysis (TICA), which micro-costs government expenses by tracking every employee activity associated with a particular program or policy. The time spent by each employee involved in the transaction is then multiplied by the employee s wage rate and summed for all employees. TICA has been used in comparing the costs of several problem-solving courts to standard case processing by multiplying the duration of court appearances, drug tests, treatment sessions, and other transactions by the salaries of the judges, attorneys, bailiffs, court reporters, case managers, and service providers involved in those transactions. 23 Accurate micro-costing requires the ability to differentiate between what is a programmatic element and what is business as usual. Thus, a prerequisite for micro-costing is consultation with program personnel to create a logic model or flowchart defining the program and any alternatives to which it will be compared. Some people who work in government are inclined to dismiss employee time costs for programs run by existing staff, arguing that the employees time is like a fixed cost, already paid for regardless of the activity. This notion is incompatible with CBA, a tool for determining whether a particular use of time and other readily monetizable resources is more beneficial than an alternative use. Taxpayer Costs Taxpayers fund the operations of law enforcement, courts, corrections, and various public-sector 23 Shannon Carey and Michael Finigan, A Detailed Cost Analysis in a Mature Drug Court Setting: A Cost-Benefit Evaluation of the Multnomah County Drug Court (Portland, OR: NPC Research, 2003). Vera Institute of Justice 19

24 functions linked to the justice system. Decision makers want to know how much justice policies and programs will affect government costs, so accurate estimation of these costs is critical to CBA. Consider a policy that has the effect of increasing the number of arrests for a particular crime. To conduct a CBA of such a policy, analysts would need to estimate not only the increased cost to the police, but also, depending on the type of crime, the additional costs related to the increased use of court and corrections resources for trying and incarcerating more people or supervising them (for example, through parole or probation). Taxpayer costs should be calculated using the marginal cost of each activity. The marginal cost is the amount of change in total cost when a unit of production changes. In the context of the criminal justice system, it is how much the total operating costs of an agency change when workload (such as arrests, court filings, or jail days) changes because of a policy or program. It is critical to use marginal costs in CBA calculations. One of the most fundamental errors an analyst can make is using average costs, a mistake that usually results in overestimating the costs or the avoided costs related to a policy change. This is because the average cost includes fixed costs such as administration and other overhead costs that policy changes rarely alter. (For more information, refer to Vera s Guide to Calculating Justice-System Marginal Costs. 24 ) The difference between average and marginal costs is often considerable. In 2011, for example, the average annual per-inmate cost of incarceration in Massachusetts was an estimated $46,000, whereas the marginal cost was only $9, Average cost includes costs for administration, utilities, and other expenses that will not change when the prison population is slightly reduced or increased. A small change in the population affects expenses such as food, clothing, and medical care: these are the marginal costs of a small increase or decrease in the prison population. Marginal costs depend on the size of the change in workload and how the government adjusts the budget in response to this change. This means that more than one marginal cost could potentially be used in justice CBAs. Marginal costs that change immediately with even a small change in workload are called short-run marginal costs. When a policy has a larger impact on workload, staffing costs need to be considered, yet it may take time for the government to change staffing levels. Thus, a long-run marginal cost includes the short-run marginal cost as well as the staffing costs that change as governments make adjustments to staffing levels in future budget cycles. Cost-benefit studies of criminal justice initiatives should use the long-run marginal cost when the effect of the policy on workload is expected to affect staffing needs. Analysts should use the short-run marginal cost when the policy impact is not large enough to affect staffing. 24 Christian Henrichson and Sarah Galgano, A Guide to Calculating Justice-System Marginal Costs (New York: Vera Institute of Justice, 2013), (accessed January 16, 2014). 25 Paul Heroux, Addressing the prison s budget and population, Taunton Daily Gazette, February 11, Vera Institute of Justice 20

25 Keep in Mind The outcomes of criminal justice policies and programs sometimes affect spending in other areas of government. Longer sentences for offenders who have children may increase costs for the foster care system; releasing elderly inmates to alleviate overcrowding may impose a greater burden on statesubsidized health care for the indigent; and reduced enforcement of truancy laws might cost public schools money. A careful CBA should take into account these possible effects beyond the criminal justice system. Cost-benefit studies usually report the total taxpayer cost as one figure. However, it may be helpful to report costs by the level of government that is, federal, state, or local. In some instances, it might also be appropriate to disaggregate taxpayer costs by unit of government, such as a corrections or social services department. Costs of Crime Crime causes substantial financial, psychological, and physical harm. Over the past few decades, economists have used a variety of methods to assign dollar values to these harms and estimate the costs of crime to victims, society as a whole, and offenders themselves. This section discusses the costs each group bears, describes the methods used for estimating them, provides cost estimates from recent studies, and discusses the costs of crime in the context of cost-benefit analysis. Victim Costs Victim costs, also referred to as victimization costs, are losses suffered by crime victims and include tangible and intangible costs. Tangible costs are those that easily translate into financial losses, such as medical costs, reduced income, and damaged or stolen property incurred because a person was the victim of a crime. Intangible costs refer to losses such as pain, suffering, and reduced quality of life that a crime victim may experience and are usually harder to monetize than tangible losses. CBAs of criminal justice policies need estimates of the dollar value of these intangibles. Such non-market goods obviously have value, but how much is not so obvious, which is why these values are called shadow prices in economic parlance. The Bottom-Up Approach Placing a dollar value on victimization can be challenging, but several studies have estimated tangible and intangible victim costs by using the cost-of-illness or jury-compensation methods. The cost-ofillness approach measures direct, tangible costs like medical expenses (obtained from hospital databases) and lost earnings. The jury-compensation approach uses the money awarded to victims by juries to estimate the indirect or intangible victim costs of crime. Relying as they do on adding up the costs in actual criminal cases, the cost-of-illness and jury-compensation methods are collectively referred to as the bottom-up approach to estimating victim costs. The main strength of the bottom-up approach is that these estimates are based on amounts that were paid to plaintiffs in an effort to make them whole. This allows for offense-specific estimates and also illustrates that the victimization cost of a crime like assault or burglary is highly skewed. In Vera Institute of Justice 21

26 most cases the physical, psychological, and financial harm is minor, but a relatively few extreme cases (for example, when a crime leaves the victim paralyzed) greatly increases the average victimization cost to many times the median victimization cost. But the approach also has drawbacks. Some jury awards incorporated in bottom-up estimates were from cases involving death or bodily injury that was not the result of a crime, so using this method assumes that juries award the same amount for injuries regardless of whether they were caused by a defective product, medical malpractice, or a criminal act. In addition, crimes adjudicated in civil court that result in jury awards may not be representative of all crimes, and research has shown regional and plaintiff gender bias in jury awards. Figure 6 summarizes the tangible and intangible victim cost estimates from jury compensation and cost-of-illness studies. All values are expressed in 2012 dollars. Figure 6. Estimated tangible plus intangible victim costs per crime using a bottom-up approach Offense Category Roman (2010) Cohen & Piquero (2009) McCollister, French & Fang (2010) Murder/Manslaughter $1,673,679 $5,106,000 $9,032,940 Rape $167,236 $149,850 $213,617 Armed robbery $320,543 a $32,190 n/e Robbery n/e $13,320 $24,155 Aggravated assault $155,260 b $41,070 $101,675 Assault n/e $4,995 n/e Arson n/e $63,270 $5,492 Larceny/Theft $2,841 $500 $11 Motor vehicle theft $20,301 $6,105 $280 Burglary (household) $5,945 $2,220 $343 Fraud n/e $1,221 n/e Vandalism n/e $411 n/e n/e = Not estimated Sources: John K. Roman, How Do We Measure the Severity of Crimes? New Estimates of the Cost of Criminal Victimization, Measuring Crime and Criminality: Advances in Criminological Theory 17, no. 37 (2010), 37-70; Mark A. Cohen and Alex R. Piquero, New Evidence on the Monetary Value of Saving a High Risk Youth. Vanderbilt Law and Economics Research Paper No , Journal of Quantitative Criminology 25, no. 1 (2009), p. 33, Table 5; and Katherine E. McCollister, Michael T. French, and Hai Fang, The Cost of Crime to Society: New Crime-Specific Estimates for Policy and Program Evaluation, Drug and Alcohol Dependence 108, nos. 1-2 (2010), p. 105, Table 5, (accessed February 3, 2014). The authors estimates have been adjusted to 2012 dollars using the Bureau of Labor Statistics Consumer Price Index Inflation Calculator, accessed August 21, a This calculation uses Roman s estimate for robbery for the cost of armed robbery. b This calculation uses Roman s estimate for assault for the cost of aggravated assault. The Top-Down Approach The cost of crime to society is not the same as the sum of victimization costs across all crime categories in a jurisdiction, because crime imposes costs even on those not directly victimized. In a Vera Institute of Justice 22

27 broad sense, crimes are a transgression against all members of society, a belief underscored by the legal convention that prosecutors represent The People in criminal trials. Economists use two methods to estimate societal costs of crime: contingent valuation and hedonic pricing. Whereas the bottom-up approach of cost-of-illness and jury-compensation methods attempts to sum up the costs borne by crime victims specifically, contingent valuation and hedonic pricing methods attempt to capture the cost of crime to society as a whole or the value society places on the amenity of public safety. As such, these methods are referred to as top-down approaches to estimating crime costs. The contingent valuation method relies on surveys that ask people to place a dollar value on changes in crime levels. A survey might ask individuals how much they are willing to pay for a 26, 27 reduction in crime, or how much they would have to be compensated for an increase in crime rate. In theory, contingent-valuation estimates encompass victimization costs, tangible crime avoidance costs, and the associated intangible fear of crime felt by potential victims and even offenders. Contingent-valuation estimates therefore tend to be significantly higher than jury award and cost-ofillness estimates. Contingent valuation has at least three limitations. First, there is some debate as to which costs survey respondents actually take into account tangible or intangible. Second, contingent-valuation surveys may overstate what people are willing to pay to avoid crime because respondents don t have to pay the amount they specify. Third, the surveys require respondents to make a difficult appraisal the valuation of a minute reduction in the risk of victimization the type of assessment usually left to actuarial statisticians. Yet despite its flaws, leading economists are of the opinion that the contingent-valuation shadow price of a crime is less precise but more correct than estimates based on a bottom-up approach. Because contingent valuation attempts to measure crime s effect on property values, the monetary value of fear of crime, social degradation, and avoidance behavior by potential victims, shadow prices based on contingent valuation present more of a neighborhood or community perspective on crime. In addition to contingent-valuation surveys, researchers use hedonic pricing to infer how individuals value public safety. Hedonic prices are determined by collecting information about property characteristics, such as square footage and number of bedrooms for homes across a large area (such as a city or county) and neighborhood characteristics, such as public school quality and access to transit. This information is then put into a regression model to tease out or unbundle the portion of area property values attributable to intangible benefits like less noise pollution. Several studies have estimated the dollar value residents place on crime and potential victimization by examining the effects of crime on property values. 28 Shadow prices developed through hedonic pricing typically do not apply to specific types of offenses, such as rape or theft, but to broader categories of violent and property crime or to crime 26 Mark A. Cohen and Alex R. Piquero, New Evidence on the Monetary Value of Saving a High Risk Youth, Vanderbilt Law and Economics Research Paper No Journal of Quantitative Criminology 25, no. 1 (2009), Mark A. Cohen, Pain, Suffering, and Jury Awards: A Study of the Cost of Crime to Victims, Law & Society Review 22, no. 3 (1988), , (accessed February 3, 2014). 28 Allen K. Lynch and David W. Rasmussen, Measuring the impact of crime on house prices, Applied Economics 33, no. 15 (2001), Vera Institute of Justice 23

28 overall. An exception is the victim cost of murder, which has been calculated by hedonic pricing applied to the labor market. Wage differentials can be used to determine how much workers in occupations like mining and construction need to be compensated to accept jobs that involve a risk of death. These compensation premiums are used to calculate the value of a statistical life, which serves as the estimate of the victim cost of murder. Hedonic pricing also has its limitations. It assumes that all of the components of property value can be unbundled, and that buyers consciously select from those components. Perhaps more problematic, most hedonic pricing studies naturally assume that crime affects property values, but neglect to consider that property values can also affect crime. The potential gains to burglary are apt to be higher in wealthy neighborhoods. On the other hand, criminals are known to commit most of their crimes close to home, and high-risk parolees, for example, are unlikely to possess the means to live in affluent areas of town. Failure to account for potential reverse causality will bias the value of public safety estimated from home prices. Last, a majority of hedonic pricing studies report the estimated value of an incremental change in the crime rate, but a few hedonic pricing researchers have eschewed crime rate as the variable of interest. These researchers instead use crime density (the number of crimes per unit area), which some have shown is a better predictor of fear of crime and property values. 29 Figure 7 summarizes the tangible and intangible societal cost estimates from contingent-valuation studies. All values are expressed in 2012 dollars. Figure 7. Estimated tangible plus intangible societal costs per crime using a top-down approach Cohen & Piquero (2009) Murder/Manslaughter $13,098,000 Rape $321,900 Armed robbery $310,800 Robbery $43,290 Aggravated assault $94,350 Assault $21,090 Arson $127,650 Larceny/Theft $4,440 Motor vehicle theft $18,870 Burglary (household) $38,850 Fraud $6,105 Vandalism $2,220 Source: Mark A. Cohen and Alex R. Piquero, 2009, p. 33, Table 5. The authors estimates have been adjusted to 2012 dollars using the Bureau of Labor Statistics Inflation Calculator, (accessed August 21, 2013). 29 Keith Ihlanfeldt and Tom Mayock, Panel data estimates of the effects of different types of crime on housing prices, Regional Science and Urban Economics 40, nos. 2-3 (2010), Vera Institute of Justice 24

29 Using Victim and Societal Cost-of-Crime Estimates in a CBA A comprehensive cost-benefit analysis of an initiative that affects crime, whether directly or indirectly, should take into account the shadow price of crime. If a policy or program reduces crime, the costs of avoided victimization and fear of crime are counted as benefits. If a policy or program increases crime, victim costs are incurred. When reporting on victim and societal costs of crime in a CBA, be explicit about the method used to obtain the estimates. Top-down cost estimates are more appropriate for CBA because they are more comprehensive than estimates based on a bottom-up approach and provide a measure of the full externality of criminal offenses on society. The disadvantage of using top-down estimates is that they reduce transparency because estimates are harder to disaggregate into types of costs. Figure 8 illustrates this problem in comparing the elements of bottom-up and top-down victim-cost estimates for the crime of rape. Both analysts and policymakers may find the greater precision of bottom-up estimates appealing, but should be aware of the important costs these estimates fail to capture. Figure 8. Victim costs of rape: Comparison of bottom-up vs. top-down approaches Bottom-Up Approach Top-Down Approach Out-of-pocket costs $650 Included Lost productivity $2,800 Included Counseling $2,800 Included Pain and suffering $103,500 Included Justice-system costs $3,250 Included Avoidance costs; prevention costs; fear Not included Included Total $113,000 $237,000 Source: Mark A. Cohen, Economic Cost of Crime and Public Policy Decisions (presentation at seminar on The Costs of Crime and Violence in Latin America and the Caribbean: Methodological Innovations and New Dimensions, Inter-American Development Bank, Washington, DC, Jan. 24, 2013, accessed February 4, 2014). Readers of your CBA may be skeptical about intangible costs, especially given that for certain types of offenses, intangible costs can be several times larger than tangible costs. Presenting tangible and intangible costs in separate columns, as in Figure 9, provides a more complete and comprehensible view of victim costs, and shows that the full cost of victimization for serious violent crimes is much higher than the tangible costs alone. Vera Institute of Justice 25

30 Figure 9. Victim costs of crime 30 VICTIM COSTS ($) Type of Offense Tangible Intangible Total Murder $737,517 $8,442,000 $8,442,000 Rape/Sexual assault $5,556 $199,642 $205,085 Aggravated assault $8,700 $95,023 $96,254 Robbery $3,299 $22,575 $24,211 Motor vehicle theft $6,114 $262 $6,352 Arson $11,452 $5,133 $16,127 Household burglary $1,362 $321 $1,653 Larceny/Theft $480 $10 $489 Source: Katherine E. McCollister, Michael T. French, and Hai Fang, The Cost of Crime to Society: New Crime-Specific Estimates for Policy and Program Evaluation, Drug and Alcohol Dependence 108, nos. 1-2 (2010), , (accessed February 3, 2014). Keep in Mind The studies and methods described above represent some of the best available research on victim and societal crime costs, but further research is needed. Notice that most studies do not calculate these costs for drug offenses, even though substance abuse affects health outcomes and generates medical costs for users and society. Cohen and Piquero have estimated the lifetime cost of a heavy drug user at $1.15 million to $1.3 million, and though the user bears much of the cost, this figure does include the cost of child neglect and endangerment. 31 Likewise, there are few estimates on social costs of whitecollar crimes, even though the tangible victim costs inflicted by fraud, insider trading, and other forms of business-related theft may far exceed the tangible costs of street crimes. Future research should address these limitations. In the meantime, using current victim or societal cost-of-crime figures is better than excluding these estimates from cost-benefit analysis. Offender Costs All government spending has an opportunity cost, that is, an alternative is forgone because money is spent on a given program or service and not on something else. To aid in the evaluation of programs 30 For most offenses, values in the Total column do not equal the sum of the values in the Tangible and Intangible columns. The tangible and intangible costs of murder are estimated using two distinct methodologies. Tangible costs reflect the value of forgone lifetime earnings, whereas intangible costs represent the value of a statistical life. (See W. Kip Viscusi and Joseph E. Aldy, The Value of a Statistical Life: A Critical Review of Market Estimates Throughout the World, The Journal of Risk and Uncertainty 27, no. 1 [2003], 5-76, accessed February 21, 2014.) The two estimates should not be combined to create a total victim cost per murder. The tangible and intangible costs for the other offenses listed in Figure 9 include valuations of the risk of homicide costs attributable to those crimes; therefore the total victim costs were adjusted to avoid double-counting. For more information about these calculations, see Katherine McCollister et al., Cohen and Piquero, 2009, 46. Vera Institute of Justice 26

31 that decrease recidivism or divert offenders from prison altogether, researchers have calculated the opportunity cost of incarceration. They do this either by using survey data on the legitimate earnings of offenders prior to imprisonment or by assuming full-time employment at minimum wage, yielding estimates of $14,626/year (1997 USD) and $13,624/year (2008 USD), respectively (see Figure 10). Justice programs may help offenders become more productive. Statistically, an offender is more likely to achieve a higher salary with gains in educational attainment and vocational training, and is more likely to have a longer and more productive career with increased health and longevity. If an adolescent is diverted from a life of chronic drug abuse, for example, this saves an estimated $43,500 in productivity loss due to drug-related illness and $125,000 to $220,000 in productivity loss due to premature death. 32 Figure 10. Estimated costs of crime to offenders, in forgone earnings McCollister et al. (2010) Cohen & Piquero (2009) Rajkumar & French (1997) Murder/Manslaughter $158,954 $155,400 n/e Rape $9,857 $4,995 n/e Armed robbery n/e $8,880 n/e Robbery $4,571 $4,440 $1,747 Aggravated assault $2,275 $7,104 $1,284 Assault n/e $1,443 n/e Arson $625 $777 n/e Larceny/Theft $174 $777 $74 Motor vehicle theft $592 $1,110 $186 Burglary (household) $729 $1,110 $366 Embezzlement $706 n/e n/e Fraud $706 $777 n/e Stolen property $1,211 n/e n/e Forgery $706 n/e $183 Vandalism $750 n/e n/e n/e = Not estimated Sources: McCollister, French, & Fang (2010), page 7, Table 4; Cohen & Piquero (2009), page 33, Table 5. Rajkumar & French (1997), page 301, Table 1. These authors estimates have been adjusted to 2012 dollars using the Bureau of Labor Statistics Consumer Price Index Inflation Calculator (http://www.bls.gov/data/inflation_calculator.htm; accessed August 21, 2013.) In CBA, offenders also have standing with respect to policies that involve due process or other constitutional issues. Empirical inquiry into the cost of due-process violations is lacking, and it is not altogether clear how analysts would quantify these concepts. As with other indirect or intangible costs, the challenge of quantifying the cost should not prevent considering these important impacts in a qualitative sense. 32 Ibid, p. 44. Vera Institute of Justice 27

32 Time Horizons and Discounting Justice system programs and policies can generate costs and benefits over a span of several years, decades, or indefinitely. In CBA, the period over which benefits and costs are assessed is referred to as the time horizon or the time frame. For example, the time horizon for a new jail could be 30 years, the expected period that the facility will be operating. To compare projects with different time horizons or even projects with the same time horizon but with different timing of cost and benefit streams economists use a technique called discounting. Discounting adjusts future costs and benefits downward, by an annual percentage called the discount rate, in recognition of the time value of money. This is the concept that money is worth more now than later, whether it is spent for immediate gratification or invested for profit. For this reason, CBA results should be expressed in terms of net present value, the net difference in costs and benefits after discounting. Selecting appropriate discount rates and time horizons is important because they can affect costbenefit results and thus policy recommendations. The higher the discount rate, the more steeply future costs and benefits depreciate. A lower discount rate decreases the value of future costs and benefits less, keeping them closer to current dollar values. A time horizon that does not capture future costs and benefits can make a project s return on investment seem better or worse than it is. Figure 11 demonstrates how time horizons and discount rates affect cost-benefit findings and the resulting policy recommendations. Consider two programs, A and B. The only cost for Program A is an upfront investment of $45,000, while Program B has upfront costs of $20,000, followed by annual costs of $2,500. Both programs yield $6,000 per year in benefits. Figure 11 shows the net present value for each program using three discount rates 0, 3, and 7 percent and computes the net present value at 10-year and 20-year time horizons. When the time value of money is taken into account, Program B, with its lower initial costs, has a higher net present value than Program A at year 10. If the discount rate is 7 percent, Program A does not even recoup its initial costs after 10 years. However, by year 20, the ongoing annual costs of Program B have diminished the net present value, and Program A looks like a better option, underscoring the importance of the time horizon choice. Vera Institute of Justice 28

34 Keep in Mind Ideally, CBAs should measure costs and benefits for as long as they persist, but in practice may estimate them for a shorter duration. Few CBAs estimate intergenerational impacts of investments even when they are viewed as potentially sizeable, because of the practical impediments to carrying out such a long-term evaluation. Analysts sometimes avoid the issue of time horizons and discounting by estimating the net benefits on a within-year basis. This is reasonable for programs with stable costs and benefit streams that are roughly contemporaneous. CBAs of programs with large start-up costs and/or significant lag time between costs incurred and benefits realized should clearly document the time horizon and discount rate used in the analysis, because the effect of discounting becomes more pronounced over longer time frames. Political realities may influence elected officials to favor programs that promise net benefits in the short term, even if another option might offer greater benefits in the long term. Finally, recognize that a long time horizon implies a strong assumption about the state of the world remaining fairly constant. The pace of innovation may render public safety technologies obsolete much more quickly than anticipated, or an unforeseen demographic shift may cause a correctional program s reach and therefore its impact to fall short of projections. This uncertainty highlights the need for challenging the inputs and assumptions of CBA, a topic discussed in Section IV. Section IV: Dealing with Uncertainty in Impacts Some degree of uncertainty is inherent in even the most rigorous cost-benefit study. The impacts of policies or programs may be difficult to measure or predict, and the value of those impacts may be hard to monetize. Cost-benefit studies must make assumptions and use estimates to calculate the expected costs and benefits of a policy or program. But what if the assumptions and estimates are off? Would different information change the bottom line results drastically, slightly, or not at all? A cost-benefit study isn t meant to guarantee precise costs and benefits, and will be misleading if its results are calculated using only point estimates for the CBA model s inputs. A CBA that considers a range of possible scenarios and explains the likely outcomes will not only be more credible than one that looks only at the base-case scenario, but more informative. Sensitivity analysis is a tool for assessing a cost-benefit model s tolerance to deviations from the base-case scenario. It can also be used to calculate the likelihood of achieving a certain result, for example, the probability that a project will break even. This section provides an overview of sensitivity analysis and describes four types: partial sensitivity analysis, best-case and worst-case scenarios, break-even analysis, and Monte Carlo analysis. Sensitivity Analysis Sensitivity analysis is a group of techniques that can be used to examine the degree of uncertainty in a CBA and how that affects a study s results. Sensitivity analysis provides a way to show how a study s Vera Institute of Justice 30

35 results would be affected and how responsive or sensitive those results would be to changes in the values of specific variables. To illustrate the different types of sensitivity analysis, consider a cost-benefit analysis of a hypothetical prison education program for first-time offenders that aims to improve employment outcomes and reduce recidivism. This CBA examines only the recidivism outcome. For purposes of this CBA, recidivism is defined as a reconviction, and the program s impact on recidivism is assumed to apply equally across offense categories. The program s recidivism impact is estimated relative to three-year reconviction patterns for a cohort of inmates who would have been eligible for the program but were released just before it started. Bottom-up estimates similar to those discussed in Section III are used to approximate recidivism costs. Recidivism impact, estimated from tracking post-release outcomes for the earliest cohort of participants, is a statistically significant 12 percent decrease ( 12%), but standard errors for the estimate are large, indicating that the recidivism impact may fall anywhere between a 25 percent decrease ( 25%) and a 2 percent decrease ( 2%). Program attrition is a cause for concern, because participants who don t complete the program appear to fare no better than the comparison group post-release. Program cost varies by individuals needs but is known to range from $2,000 to $4,000 with a mean of $2,500. The base-case scenario is shown in Figure 12. Figure 12. Base-case scenario for a hypothetical prison education program BASELINE RECONVICTION RATE PER 100 PAROLEES Year 1 Year 2 Year 3 Justice System Victim (Tangible) COST PER RECONVICTION Victim (Intangible) Offender Total Homicide $385,040 $592,000 $6,776,300 $250,000 $8,003,340 Sex assault $91,800 $21,644 $772,170 $50,000 $935,614 Robbery $61,850 $7,800 $11,770 $15,000 $96,360 Assault $36,680 $22,680 $35,000 $40,000 $134,360 Property crime $20,200 $5,000 $0 $10,000 $35,200 Drug crime $13,100 $0 $0 $3,000 $16,100 Other crime $24,280 $0 $0 $750 $25,030 Program costs (100 participants) ($232,000) ($232,000) Program benefits $93,866 $18,673 $161,000 $32,396 $305,955 Net present value ($138,114) $18,673 $161,000 $32,396 $73,955 Note: The average program cost per graduate is $2,500. The average program cost per nongraduate is $500. The program attrition rate is 9%, and the expected three-year impact on recidivism for program graduates is a 12% decrease. The discount rate used in calculations is 3%. Partial Sensitivity Analysis In a partial sensitivity analysis, variables are selected one at a time and their values are changed while holding the values of other variables constant to see how much the CBA results change in response. Vera Institute of Justice 31

36 Figure 13 illustrates the results of a basic partial sensitivity analysis that looks at the effect of changes in recidivism on the net benefit of the hypothetical prison education program when program costs and other inputs are held constant. The figure indicates that a 25 percent reduction in recidivism would lead to a net benefit of more than $405,000, a 15 percent reduction would result in a net benefit of $150,000, and a 2 percent reduction would generate a net cost of $181,000. Figure 13. Partial sensitivity analysis results for a hypothetical prison education program Change in Recidivism 2% 10% 15% 20% 25% Program costs $232,000 $232,000 $232,000 $232,000 $232,000 Program benefits $51,000 $255,000 $382,450 $510,000 $637,400 Net benefit $181,000 $23,000 $150,450 $278,000 $405,400 Conducting a partial sensitivity analysis for all variables can highlight the ones that have the greatest impact on the program s net present value. This information is sometimes conveyed graphically using what s called a tornado diagram. This is a bar chart that ranks model inputs by the amount the net benefit deviates from base-case estimates over a percentile (or percentage deviations) of the inputs. Figure 14 depicts a tornado diagram of the hypothetical prison education program. The central axis is the expected net present value of the program, about $74,000 for 100 participants, as shown in Figure 12; the bars show how much the net present value increases (to the right) or decreases (to the left) within the 5th and 95th percentile range for the variables indicated, holding all other variables constant at the base-case scenario value. The recidivism impact, the program cost, and the rate of reconvictions for serious violent crime are the variables that have the greatest influence on the net present value, and only recidivism impact and program cost have enough influence to push the program s net present value below zero. Vera Institute of Justice 32

37 Figure 14. Tornado diagram of a hypothetical prison education program (10 most-influential variables) Legend Value of variable is increasing. Value of variable is decreasing. Note: The total net present value of the hypothetical prison education program for 100 participants is $73,955. Vera Institute of Justice 33

38 Best-Case and Worst-Case Scenarios These scenarios establish the upper (best-case) and lower (worst-case) boundaries of a cost-benefit study s results. This type of sensitivity analysis shows how a broad range of a program s possible outcomes affects the bottom line. The best-case scenario uses all of the most-favorable assumptions about the program s outcomes, such as the upper limit of the estimated effect on recidivism and the lowest projected program attrition rate; the worst-case scenario uses all of the least-favorable assumptions, such as the lower limit estimate of recidivism impact and the highest projected program attrition rate. For the prison education program, the best-case scenario when accounting only for the impact on recidivism is that recidivism decreases by 25 percent. As Figure 13 shows, this would yield a net benefit of more than $405,000 for 100 participants. If discount rate and program cost are also near their lower bounds, and if baseline recidivism rates and crime costs are near their peak values, the net present value increases to only about $440,000. Conversely, the most unfavorable assumptions result in a net present value of approximately $340,000. To project best and worst outcomes that are plausible, refer to existing research on similar programs, possibly including a pilot study of the program under consideration. Keep in mind that bestcase and worst-case scenarios are extreme cases; the odds of them occurring should be low unless input estimates are badly off the mark. Break-Even Analysis Break-even analysis can be used in the following situations: if the goal is to determine a policy s minimum necessary effectiveness; if the policy s most likely effects cannot be estimated; or if there are no comparable studies to help determine the policy s best-case and worst-case scenarios. This type of sensitivity analysis estimates what effect size a policy must have for its benefits to equal its costs, that is, to break even. By definition, breaking even results in a net benefit of $0. If exceeding the break-even point is feasible, benefits would potentially outweigh the costs. If reaching the break-even point is not feasible, the costs are likely to exceed the benefits. Consider the hypothetical prison education program: According to the CBA model, the policy would need to reduce recidivism by 9.1 percent to be economically neutral. As with partial sensitivity analysis and best-case and worst-case scenario analysis, break-even analysis can be applied to inputs other than program effect size. It is not uncommon, especially in the financial industry, for cost-benefit studies to solve for the discount rate at which the program breaks even as an index of its profitability. In this case, a percent break-even discount rate indicates that the program is a good investment (under the base-case scenario) because the break-even discount rate is much higher than the prevailing time value of money The guideline at the Office of Management and Budget, the Executive branch office that evaluates federal programs using CBA, is to set the discount rate at 3 and 7 percent for CBAs of non-regulatory programs. See Office of Management and Budget, Circular A-94: Guidelines and Discount Rates for Benefit-Cost Analysis of Federal Programs (Washington, DC: OMB, 1992), (accessed February 3, 2014). Vera Institute of Justice 34

39 Monte Carlo Analysis The three methods previously described may not fully characterize the uncertainty associated with a cost-benefit study, because they convey little about the likelihood of certain program outcomes. For this, Monte Carlo analysis can be helpful. This method can be used to examine multiple variables simultaneously and simulate thousands of scenarios, resulting in a range of possible outcomes and the probabilities that they will occur. Monte Carlo simulation repeatedly draws random values for each model input to create a probability distribution of outcomes. The CBA results can then be expressed as the estimated probability that the program will yield net benefits or as the range of forecasted net present values within some confidence interval. To illustrate, consider again the prison education program for first-time offenders. Suppose the program cost varies by individuals needs but is known to range from $2,000 to $4,000, with a mean of $3,000. Figure 15 depicts the probability distribution of net present values for 3,000 simulations of this hypothetical program. The distribution indicates that the program is forecast to have a 60 percent probability of achieving a positive net present value (i.e., a percent certainty of net present value results that are cost-neutral or better, colored blue). The 5th and 95th percentiles are marked as vertical lines, indicating that the program has a 5 percent chance of a net present value exceeding $283,063 and an equal probability of a net present value of less than $175,702. A risk-averse agency might therefore opt against making this investment in first-time offenders. Note, however, that the calculus might change if the analysis included the program s impact on employment earnings. Monte Carlo simulations can be done with some effort using ordinary spreadsheet applications. Special statistical software packages can be used to conduct the analysis in seconds, which can make the procedure seem seductively simple. Recognize that Monte Carlo analysis increases data requirements. The outcome probability distribution it produces is only as reliable as the model and the variable distributions that serve as inputs, so if the effect-size estimate is biased, the Monte Carlo results will be too. Vera Institute of Justice 35

40 Figure 15. Monte Carlo analysis results for a hypothetical prison education program Keep in Mind Sensitivity analysis is part of making cost-benefit analysis transparent to others. Although the idea may seem paradoxical, practitioners come across as more dependable when they explicitly address uncertainty. A CBA report should clearly state its underlying assumptions, how estimates were obtained, and where potential errors might exist. Section V: Making CBAs Clearer and More Accessible Cost-benefit analyses can be difficult to understand, and the technical details of a study may be complex and hard to follow. But analysts should strive to make their reports clear and accessible. Doing so enhances the value of their work by making their studies more useful to policymakers as well as other analysts. Figure 16 summarizes the recommendations that Vera s Cost-Benefit Analysis Methods Working Group made for improving the clarity of cost-benefit studies. (For more about the working group, see About This Paper before the Table of Contents.) Vera Institute of Justice 36

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